AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time
	1_DecisionTree	Decision Tree	rmse	0.17655	0.68
	2_Baseline	Baseline	rmse	0.705007	0.69
	3_DecisionTree	Decision Tree	rmse	0.169124	0.66
	4_DecisionTree	Decision Tree	rmse	0.169124	0.65
	5_Linear	Linear	rmse	0.185766	0.67
	6_Default_Xgboost	Xgboost	rmse	0.148858	0.79
	7_Default_NeuralNetwork	Neural Network	rmse	0.149428	0.77
	8_Default_RandomForest	Random Forest	rmse	0.115134	1.06
	9_Xgboost	Xgboost	rmse	0.133685	0.85
	18_RandomForest	Random Forest	rmse	0.120136	1.03
	27_NeuralNetwork	Neural Network	rmse	0.170187	0.79
	10_Xgboost	Xgboost	rmse	0.157346	0.79
	19_RandomForest	Random Forest	rmse	0.133101	1.19
	28_NeuralNetwork	Neural Network	rmse	0.168559	0.77
	11_Xgboost	Xgboost	rmse	0.188765	0.86
	20_RandomForest	Random Forest	rmse	0.131749	1.13
	29_NeuralNetwork	Neural Network	rmse	0.182241	0.75
	12_Xgboost	Xgboost	rmse	0.700421	0.78
	21_RandomForest	Random Forest	rmse	0.155729	1.05
	30_NeuralNetwork	Neural Network	rmse	0.170635	0.73
	13_Xgboost	Xgboost	rmse	0.274347	0.9
	22_RandomForest	Random Forest	rmse	0.117702	1.03
	31_NeuralNetwork	Neural Network	rmse	0.228088	0.73
	14_Xgboost	Xgboost	rmse	0.150953	0.8
	23_RandomForest	Random Forest	rmse	0.135651	1.1
	32_NeuralNetwork	Neural Network	rmse	0.207833	0.75
	15_Xgboost	Xgboost	rmse	0.148054	0.82
	24_RandomForest	Random Forest	rmse	0.167455	1.41
	33_NeuralNetwork	Neural Network	rmse	0.227885	0.76
	16_Xgboost	Xgboost	rmse	0.182733	0.8
	25_RandomForest	Random Forest	rmse	0.134702	1.05
	34_NeuralNetwork	Neural Network	rmse	0.451462	0.77
	17_Xgboost	Xgboost	rmse	0.174338	0.84
	26_RandomForest	Random Forest	rmse	0.127359	1.08
	35_NeuralNetwork	Neural Network	rmse	0.243122	0.76
	8_Default_RandomForest_GoldenFeatures	Random Forest	rmse	0.123566	7.67
	22_RandomForest_GoldenFeatures	Random Forest	rmse	0.136833	1.46
	18_RandomForest_GoldenFeatures	Random Forest	rmse	0.135679	1.12
	8_Default_RandomForest_KMeansFeatures	Random Forest	rmse	0.12095	1.28
	22_RandomForest_KMeansFeatures	Random Forest	rmse	0.118526	1.25
	18_RandomForest_KMeansFeatures	Random Forest	rmse	0.118778	1.23
	8_Default_RandomForest_RandomFeature	Random Forest	rmse	0.119407	5.55
	36_RandomForest	Random Forest	rmse	0.115134	1.53
	37_RandomForest	Random Forest	rmse	0.114405	1.09
	38_RandomForest	Random Forest	rmse	0.164353	1.09
	39_RandomForest	Random Forest	rmse	0.117702	1.13
	40_RandomForest	Random Forest	rmse	0.118526	1.24
	41_RandomForest	Random Forest	rmse	0.125044	1.79
	42_Xgboost	Xgboost	rmse	0.132654	1.06
	43_Xgboost	Xgboost	rmse	0.134524	0.96
	44_Xgboost	Xgboost	rmse	0.149779	0.92
	45_Xgboost	Xgboost	rmse	0.146899	0.9
	46_Xgboost	Xgboost	rmse	0.149926	0.89
	47_NeuralNetwork	Neural Network	rmse	0.263983	0.81
	48_NeuralNetwork	Neural Network	rmse	0.159261	0.83
	49_NeuralNetwork	Neural Network	rmse	0.173453	0.88
	50_NeuralNetwork	Neural Network	rmse	0.194293	0.82
	51_DecisionTree	Decision Tree	rmse	0.141352	0.75
	52_RandomForest	Random Forest	rmse	0.116809	1.13
	53_RandomForest	Random Forest	rmse	0.114405	1.18
	54_RandomForest	Random Forest	rmse	0.116578	1.16
	55_RandomForest	Random Forest	rmse	0.115134	1.17
	56_RandomForest	Random Forest	rmse	0.116578	1.21
	57_RandomForest	Random Forest	rmse	0.115134	1.2
	58_Xgboost	Xgboost	rmse	0.165005	0.93
	59_Xgboost	Xgboost	rmse	0.164295	0.94
	60_Xgboost	Xgboost	rmse	0.164177	0.94
	61_NeuralNetwork	Neural Network	rmse	0.152606	0.86
	62_NeuralNetwork	Neural Network	rmse	0.241705	0.86
	63_NeuralNetwork	Neural Network	rmse	0.265339	0.87
	64_NeuralNetwork	Neural Network	rmse	0.215439	0.89
the best	Ensemble	Ensemble	rmse	0.0971189	8.26

AutoML Performance

AutoML Performance Boxplot

Spearman Correlation of Models

models spearman correlation

Metric	Score
MAE	0.130615
MSE	0.0247578
RMSE	0.157346
R2	0.947665
MAPE	0.222804

Metric	Score
MAE	0.13997
MSE	0.0356321
RMSE	0.188765
R2	0.924679
MAPE	0.20243

Metric	Score
MAE	0.594858
MSE	0.49059
RMSE	0.700421
R2	-0.037042
MAPE	1.71653

Metric	Score
MAE	0.220566
MSE	0.0752663
RMSE	0.274347
R2	0.840897
MAPE	0.359979

Metric	Score
MAE	0.095322
MSE	0.0227869
RMSE	0.150953
R2	0.951831
MAPE	0.120676

Metric	Score
MAE	0.0729458
MSE	0.00943209
RMSE	0.0971189
R2	0.980062
MAPE	0.160801

AutoML Leaderboard

AutoML Performance

AutoML Performance Boxplot

Spearman Correlation of Models

Summary of 10_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

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Learning curves

True vs Predicted

Predicted vs Residuals

Summary of 11_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

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Learning curves

True vs Predicted

Predicted vs Residuals

Summary of 12_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

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Learning curves

True vs Predicted

Predicted vs Residuals

Summary of 13_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

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Learning curves

True vs Predicted

Predicted vs Residuals

Summary of 14_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

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Learning curves

True vs Predicted

Predicted vs Residuals

Summary of 15_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

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Learning curves

True vs Predicted

Predicted vs Residuals

Summary of 16_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

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Learning curves

True vs Predicted

Predicted vs Residuals

Summary of 17_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

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Learning curves

True vs Predicted

Predicted vs Residuals

Summary of 18_RandomForest

Random Forest

Validation

Optimized metric